Interpretable Multi-Model Framework for Early Warning of SME Loan Delinquency
Akhmetova A. Shayakhmetova A. Abdurakhmanov N.
February 2026Multidisciplinary Digital Publishing Institute (MDPI)
Risks
2026#14Issue 2
The rapid expansion of small and medium enterprise (SME) lending has intensified the need for accurate and interpretable credit risk forecasting. Financial institutions must anticipate potential business loan delinquency to maintain portfolio stability and meet regulatory standards. This study proposes an interpretable multi-model framework that integrates statistical (correlation screening and ordinary least squares regression), probabilistic (Gaussian Naïve Bayes), and classical time-series (SARIMA) methods to balance explanatory insight and predictive accuracy in delinquency forecasting. Ordinary least squares regression is used to quantify the direction and strength of each driver and yields statistically significant coefficients (β ≈ 1.336 for the overdue 15+ days bucket, p < 10−22). The Naïve Bayes classifier provides a probabilistic early-warning signal with an out-of-sample accuracy of 55%, precision of 43%, recall of 75%, and ROC AUC of 0.371. Finally, a seasonal ARIMA model fitted on the selected regressors achieves a mean absolute percentage error (MAPE) of 7.6% and an out-of-sample R2 of 0.49, demonstrating competitive forecasting performance while maintaining interpretability. The results show that the framework offers actionable insights for risk managers by identifying key risk drivers, providing probabilistic alarms, and generating calibrated point forecasts. The proposed approach contributes to the development of intelligent and explainable forecasting and control systems for modern financial institutions.
business loan delinquency , data-driven decision-making , explainable artificial intelligence , intelligent risk management systems , linear regression , Naïve Bayes , NeuralProphet , probabilistic forecasting , SME credit risk , time-series modelling
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Intelligent Control Systems Program, Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
Intelligent Control Systems Program
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026